Jenny Kim – författare

Publications on aesthetic rejuvenation often start with the treatment—such as fillers or lasers—and then work back to the patient problem. Comprehensive Aesthetic Rejuvenation: A Regional Approach starts from the perspective of what the patient perceives as the problem region of the body. It then moves to the appropriate range of treatments that can be offered and discusses how outcomes can be improved. Topics include: The new growth agent for eyelashes Multimodal approaches to healthy skin Aesthetic considerations in ethnic skin Hair loss and transplantation New developments in less invasive fat and cellulite treatments Body contouring surgery Enhanced with more than 200 color illustrations, this volume is an essential resource for all aesthetic and plastic surgeons.

Publications on aesthetic rejuvenation often start with the treatment—such as fillers or lasers—and then work back to the patient problem. Comprehensive Aesthetic Rejuvenation: A Regional Approach starts from the perspective of what the patient perceives as the problem region of the body. It then moves to the appropriate range of treatments that can be offered and discusses how outcomes can be improved. Topics include: The new growth agent for eyelashes Multimodal approaches to healthy skin Aesthetic considerations in ethnic skin Hair loss and transplantation New developments in less invasive fat and cellulite treatments Body contouring surgeryEnhanced with more than 200 color illustrations, this volume is an essential resource for all aesthetic and plastic surgeons.

The best source for the latest treatments-and combinations of treatments-for all procedures of the face and body.This is not just another textbook: starting from the patient and the problem body region the experts advise how to tailor what is available to what is required and consequently how to improve outcomes.

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you'll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You'll also learn about the analytical processes and data systems available to build and empower data products that can handle-and actually require-huge amounts of data.Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark's MLlib

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce.

Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data.

Understand core concepts behind Hadoop and cluster computingUse design patterns and parallel analytical algorithms to create distributed data analysis jobsLearn about data management, mining, and warehousing in a distributed context using Apache Hive and HBaseUse Sqoop and Apache Flume to ingest data from relational databasesProgram complex Hadoop and Spark applications with Apache Pig and Spark DataFramesPerform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce.

Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data.

Understand core concepts behind Hadoop and cluster computingUse design patterns and parallel analytical algorithms to create distributed data analysis jobsLearn about data management, mining, and warehousing in a distributed context using Apache Hive and HBaseUse Sqoop and Apache Flume to ingest data from relational databasesProgram complex Hadoop and Spark applications with Apache Pig and Spark DataFramesPerform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib